Research On Adaptive Fault Feature Extraction Method Of Rolling Bearing Under Variable Speed Condition

Rolling bearing is an important part of rotating machinery,and its health status will directly affect the service life and operation reliability of mechanical equipment.With the development of science and technology,complex and precise mechanical equipment has put forward higher requirements for its safety and reliability.Therefore,the traditional signal processing method based on stable condition cannot be applied to the fault diagnosis of mechanical equipment under variable speed.The signal characterization of rolling bearings under variable speed condition is complex,and the non-stationary characteristics are obvious,so it is a challenging task to extract the fault features.As a fault diagnosis method for mechanical equipment with variable speed,the typical Tacholess order tracking(TLOT)method still has the problems,such as high computational complexity,low adaptability,insufficient accuracy of instantaneous frequency estimation,etc.Therefore,in order to solve the problems of the typical fault diagnosis method of rolling bearing under variable speed condition,it is urgent to develop a new adaptive method of fault feature extraction for unsteady vibration signals.Firstly,aiming at the problems of the typical TLOT method,a new adaptive instantaneous angular speed(IAS)estimation method based on the improved Viterbi algorithm(VA)and the energy centrobaric correction method is developed.Since the traditional VA cannot process the long time series and find the fundamental order automatically,an improved penalty function for ridge search is proposed.Meanwhile the search band of IAS is adaptively optimized to accelerate the speed of ridge search.Furthermore,an energy centrobaric correction approach is used for improving the accuracy of instantaneous frequency estimation and the anti-noise performance.With the obtained IAS,the nonstationary time-domain vibration signal is resampled as the stationary angle-domain vibration signal by Vold-Kalman filtering and Hilbert transform.Finally,the envelope order spectrum is used to diagnose the mechanical faults of a civil aircraft engine.And the experimental results show that the improved VA can adaptively estimate IAS faster and achieve higher accuracy of fault detection than the typical IAS estimation methods.Secondly,aiming at the problems of the typical Time-frequency analysis(TFA)method,such as weak time-frequency energy aggregation ability and high computational complexity,an improved TFA method based on Synchrosqueezed transform(SST)is proposed for Chirplet transform(CT)with adaptive optimal searching angle band.Since the traditional CT cannot process the multi-component nonlinear vibration signal,the chirp rate of CT is improved to improve the quality of time-frequency representation.Meanwhile aiming at the problem of the slow calculation speed,the search band of chirp rate is adaptively optimized to accelerate the speed of TFA.With the obtained concentrated energy results of TFA,the fault diagnosis of real rolling bearing is carried out by TLOT.And the experimental results show that the proposed method has better time-frequency energy aggregation performance and faster calculation speed than the typical TFA methods.Finally,based on the research of fault feature extraction method of rolling bearing under variable speed condition,and aiming at the demand of condition monitoring,the software system is developed on the basis of the adaptive ISA estimation method and the improved CT method by Lab VIEW.And the effectiveness of the system is validated by practical bearing data.